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|a Q360 .B786 2017
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|a HCDD
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|a Brunner, Florian D.
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|a Set-Theoretic Approaches to the Aperiodic Control of Linear Systems.
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|a Berlin :
|b Logos Verlag Berlin,
|c 2017.
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|a 1 online resource (244 pages)
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|a Intro -- 1 Introduction -- 2 Background and Preliminaries -- 2.1 Dynamical systems and stability -- 2.2 Discrete-time linear systems -- 2.3 Event-triggered and self-triggered control -- 2.4 Set-valued estimation -- 2.4.1 Set-valued estimates from linear estimator dynamics -- 2.4.2 Set-valued moving-horizon estimation -- 2.5 Model predictive control -- 3 Linear Systems Perturbed by Bounded Disturbances -- 3.1 Preliminaries -- 3.2 Lyapunov-based approach -- 3.2.1 Theoretical results -- 3.2.2 Output feedback -- 3.2.3 Computational aspects -- 3.2.4 Numerical examples -- 3.3 Set-based approach -- 3.3.1 State feedback -- 3.3.2 Analysis of given aperiodic schemes -- 3.3.3 Output feedback -- 3.3.4 Computational aspects -- 3.3.5 Numerical examples -- 3.4 Summary -- 4 Stochastic Threshold Design in Event-triggered Control -- 4.1 Threshold design for arbitrarily distributed disturbances -- 4.1.1 Probability assigment -- 4.1.2 Expected value assignment -- 4.2 Stochastic thresholds for Gau{u02C7}ian noise disturbances -- 4.2.1 State-Feedback -- 4.2.2 Output feedback -- 4.3 Summary -- 5 Aperiodic Model Predictive Control of Constrained Linear Systems -- 5.1 Lyapunov-based approach -- 5.1.1 A Lyapunov function for robust MPC -- 5.1.2 Relaxing the rate of decrease -- 5.1.3 Aperiodic control algorithms -- 5.1.4 Implementation -- 5.2 Mixed set{Lyapunov approach -- 5.2.1 Feasibility by value function decrease, stability by set-membership condition -- 5.2.2 Aperiodic control algorithms -- 5.2.3 Implementation -- 5.3 Purely set-based approach -- 5.3.1 Feasibility from set-membership conditions -- 5.3.2 Aperiodic control algorithms -- 5.3.3 Implementation -- 5.4 Threshold-based event-triggered MPC: analysis and stochastic design -- 5.5 Numerical example -- 5.6 Summary -- 6 Output-feedback Event-triggered Model Predictive Control.
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|a 6.1 Set-valued moving horizon estimation in model predictive control -- 6.1.1 General results -- 6.1.2 Realization with set-valued moving horizon estimation -- 6.1.3 Implementation -- 6.1.4 Numerical example -- 6.2 Event-triggered output-feedback control -- 6.2.1 Closed-loop properties -- 6.2.2 Implementation -- 6.2.3 Numerical Examples -- 6.2.4 Outlook: extension to self-triggered control -- 6.3 Summary -- 7 Conclusions.
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|a Long description: In this thesis, we employ set-theoretic properties of additively disturbed linear discrete-time systems to develop stabilizing aperiodically updated control laws for plants controlled over communication networks. In particular, we design event-triggered and self-triggered controllers with a priori guarantees on closed-loop characteristics such as stability, asymptotic bound, and average communication rate. Different models for the disturbances are taken into account, namely arbitrary disturbances of which only a bound in the form of a compact set is known and stochastic disturbances with known probability distribution. For setups with hard constraints on the states and inputs, we propose aperiodic schemes based on robust model predictive control methods. Both the full information (state-feedback) case, as well as the limited information (output-feedback) case are investigated. It is demonstrated that the proposed controllers achieve a considerable reduction in the required network usage with only moderate or non-existing deterioration of the closed-loop properties guaranteed by comparable controllers that transmit information at every point in time.
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|a Information theory-Methodology.
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|i Print version:
|a Brunner, Florian D.
|t Set-Theoretic Approaches to the Aperiodic Control of Linear Systems.
|d Berlin : Logos Verlag Berlin, ©2017
|z 9783832546229
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| 856 |
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|u https://ebookcentral.proquest.com/lib/holycrosscollege-ebooks/detail.action?docID=5216186
|y Click for online access
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|a EBC-AC
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